The high penetration of renewable energy imposes more stringent requirements on the balance of power system, with Demand Response (DR) has been proven to be an effective regulation approach. However, existing DR research predominantly relies on homogeneity assumptions, failing to adequately account for the significant heterogeneity in load characteristics and response behaviors among large-scale customers. This deficiency leads to uneven allocation of regulation tasks and insufficient exploitation of the overall DR potential. To address these challenges, this paper proposes a Hierarchical Differentiated Custom Directrix Load-based Demand Response (HD-CDL-DR) mechanism tailored for large-scale heterogeneous customers. First, hierarchical differentiated CDLs are generated to provide DR guiding targets, in which the System Guiding CDL (SG-CDL) satisfies system-level regulation requirements, while the Heterogeneity-Adaptive CDLs (HA-CDLs) serve as differentiated DR targets that closely match heterogeneous customer load characteristics. Second, a two-level aggregation model is developed. At the upper level, Primary Load Aggregators (PLAs) are constructed to aggregate large-scale customer features and reduce decision dimensionality. At the lower level, Secondary Load Aggregators (SLAs) are formed to exploit the complementary response potential among heterogeneous customers. Finally, a comprehensive DR capability evaluation framework is established, incorporating both response performance and customer experience to quantify the fairness of task allocation and customer participation willingness. Case study results demonstrate that the proposed mechanism fully exploits the DR potential of large-scale customers, and achieves precise matching and optimal allocation of regulation tasks across heterogeneous resources.

Virtual power plants (VPPs) can participate in electricity market trading by aggregating distributed resources, thereby providing flexible regulation services to the power system. However, the interaction between VPPs and the power system involves high-frequency, multi-party data exchange, posing significant cybersecurity risks. To ensure the cybersecurity of VPPs throughout the entire interaction process, this paper first analyzes the intrinsic attributes of VPPs, identifying three key characteristics: aggregation, interaction, and regulation. Based on this, it integrates these attributes with the confidentiality, integrity, and availability triad to construct a cybersecurity theoretical framework for VPPs. Second, this paper outlines the business processes, data interactions, and communication architecture for VPPs and power system interaction. Then, a risk analysis of potential cyberattacks is performed, detailing their injection points, propagation paths, and potential impacts of typical cyberattacks. Third, the core application mechanisms and use cases of key cybersecurity defense technologies are summarized. These technologies are applicable to interactions between VPP and power systems across different stages, including data anonymization, encryption, privacy-preserving computation, access control, and blockchain. Finally, an analysis of the current status, challenges, and prospects of artificial intelligence (AI)-based cybersecurity technologies is presented, which provides support for constructing a secure and reliable cybersecurity defense system for VPPs.

Since many countries have proposed the goal of “carbon neutrality”, the continuous implementation of low-carbon transformation strategies in the power sector has resulted in unprecedented opportunities and challenges for the development and innovation of power systems. To address this situation, China is accelerating the construction of a new type of power system that is safe, clean, and cost-efficient. As of March 2025, the installed capacity of renewable generation in China has reached 1,966 GW, accounting for 57.3% of the total installed capacity. China is vigorously promoting clean consumption, and the electrification rate of end users in the whole society will reach 30% in 2025. New types of electric loads, such as data centers, electric vehicles, and 5G base stations are growing rapidly. Although these changes have facilitated the implementation of ‘dual carbon goals’, they have also considerably impeded the operation of this new power system. The output fluctuation of renewable generation and the consumption randomness of new types of electric loads continue to exacerbate the gap in flexibility resources and supply—demand balancing while posing high-frequency, short-term, occasional events and making it difficult to predict problems with the power system. In addition, the enormous amount of distributed energy resources (DERs) is seeking to participate in the electricity market to obtain maximum operation benefits, and establishing an innovative market operation mode incorporating various massive end-users is urgently needed. Therefore, the virtual power plant (VPP) becomes the popular technical means to deal with the above problems. The constructed regulation capability of VPPs in China has been over 10GW, which becomes the important component of system flexibilities as well as extends regulation means of power system. This article introduces the technical concept of VPP and discusses their operational differences from other DERs technological forms. Based on the multidimensional perspectives of cyber-physical-social, it discusses the key technical framework to achieve the large-scale interconnection and heterogeneous coordination of VPPs in China. Taking Shanghai and Shenzhen as typical use cases, it introduces the pilot projects and demonstration achievements in mega cities, providing a solution for climate risks and energy safety issues

The scale of distributed energy resources is increasing, but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness. To address this issue, the concept of cleanness value of distributed energy storage (DES) is proposed, and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness. Based on this, an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator. Then, an optimal low-carbon dispatching for a virtual power plant (VPP) with aggregated DES is constructed, where-in energy value and cleanness value are both considered. To achieve the goal, a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network (DN) and DES behavior, but as a cost, it brings multiple nonlinear relationships. Subsequently, a solution method based on the convex envelope (CE) linear re-construction method is proposed for the multivariate nonlinear programming problem, thereby improving solution efficiency and feasibility. Finally, the simulation verification based on the IEEE 33-bus DN is conducted. The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond. Meanwhile, resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.

Virtual power plants (VPPs) have become an important technological means for large-scale distributed energy resources to participate in the operation of power systems and electricity markets. However, the operation of VPPs is challenged by stochastic resource characteristics, complex control features, heterogeneous information structures, and strategic game behaviors among stakeholders. To clarify the key problems and solutions to these challenges, this article describes the resource coordination problems and multidimensional interaction mechanism, and it elaborates the overall decision-making process of VPPs. It also discusses different specific operational stages that VPPs should attach importance to from three separate perspectives: energy, information and the market. From each perspective, every section first analyzes the motivation of decision-making, then analyzes the complexity of the problem models, and summarizes the different modeling methods and solving techniques, thus completing a comprehensive review of VPP operation. Furthermore, the article adopts an interdisciplinary approach, utilizing a literature review and technical statistics to capture the multifaceted contributions of decision-making to VPP operations. It delves into the evolving trends of decision-making technology, analyzed from the coupling cyber-physical-social perspective. Finally, the future trajectory of research issues is deliberated.

In the context of virtual power plants (VPPs), the one-size-fits-all approach of traditional static desensitization methods proves inadequate due to the diverse and dynamic operational scenarios encountered. These methods fail to provide the necessary flexibility for varying data privacy requirements across different scenarios. To address this shortcoming, our research introduces a dynamic desensitization method specifically designed for VPPs. Leveraging machine learning for adaptive scene recognition, the method adjusts data privacy levels intelligently according to each unique scenario. A novel similarity utility function and a Gaussian processes-based differential privacy algorithm ensure tailored and efficient privacy protection. Experimental results highlight an 87.5% accuracy in scene recognition, validating our method’s capability to adapt to diverse scenarios effectively. This study contributes to the field by providing a nuanced approach to data protection, effectively addressing the specific needs of complex VPP environments.

In virtual power plants, diverse business scenarios involving user data, such as queries, transactions, and sharing, pose significant privacy risks. Traditional attribute-based encryption (ABE) methods, while supporting fine-grained access, fall short of fully protecting user privacy as they require attribute input, leading to potential data leaks. Addressing these limitations, our research introduces a novel privacy protection scheme using zero-knowledge proof and distributed attribute-based encryption (DABE). This method innovatively employs Merkel trees for aggregating user attributes and constructing commitments for zero-knowledge proof verification, ensuring that user attributes and access policies remain confidential. Our solution not only enhances privacy but also fortifies security against man-in-the-middle and replay attacks, offering attribute indistinguishability and tamper resistance. A comparative performance analysis demonstrates that our approach outperforms existing methods in efficiency, reducing time, cost, and space requirements. These advancements mark a significant step forward in ensuring robust user privacy and data security in virtual power plants.

With the increasing scale of distributed energy resource (DER) allocation, local energy system (LES) has emerged as a new form of energy supply. However, the market operation mechanism of LES involving multi-level interaction still need to be revamped. Herein, firstly, an IoT-based hierarchical architecture is proposed, and the functional infrastructures of each level are presented coupling with the multilevel interaction mechanism. Then, the modeling approaches of local consumption, power sharing, and aggregation operation are depicted, especially a discriminative method for the dynamic construction of virtual power plant (VPP) is proposed. On this basis, to address the problem of computational burden and poor interpretability of centralized optimization methods involving personalized demand of massive end-users, a novel trade matching scheme is proposed. Besides that, a typical scenario generation method with Latin hypercube sampling (LHS) and k-means clustering is introduced to measure the risk of the spot market, and quantify the impact of price uncertainty on the aggregation operation decision and end-user strategic behavior. Finally, simulation validation is carried out covering diverse end-users, and the results show that the proposed method can better guide LES for local power sharing or aggregation operation, effectively reduce the operation cost, and resist the decision-making risk of spot market.

There is a consensus regarding the need to realize the transformation of renewable energy by enhancing demand-side regulating ability. This paper proposes a peak shaving potential assessment model based on the price elasticity mechanism and consumer psychology, focusing on the adjustable user load in virtual power plants. The values of deterministic parameters and the distribution of the uncertain parameter of the model are obtained through the long short-term memory network (LSTM) and mixture density network (MDN). Then, the refined distribution of peak shaving potential considering external conditions, incentive inputs, and spatial and temporal scales is obtained. Based on the evaluation results, a peak shaving decision-making model for virtual power plants is constructed using a scenario scheme. Differentiated schemes for traditional, risk-averse, and risk-seeking virtual power plant decision-makers are considered. Case studies using the data of a virtual power plant pilot area show that the proposed model can better characterize the features of virtual power plant users, and a refined control strategy with better economic benefits can be obtained.

针对新能源波动性和绿电用户随机性带来的交易匹配协同问题,提出适应虚拟电厂聚合灵活资源参与的调峰辅助市场机制及其商业运营模型,实现需求侧灵活、清洁资源优先辅助电力系统安全稳定运行.该机制设计中,将电力系统调峰市场按照传统调峰机组和第三方调峰市场主体划分为两个调峰子市场,在后者中允许虚拟电厂聚合商报量报价,引入绿色用能指数和响应成本系数,根据虚拟电厂代理资源的历史用能情况和资源核定成本修正其申报信息,得到反映绿色用能价值和资源响应成本的第三方市场主体出清序列,并根据提出的传统调峰机组与第三方市场主体的联合出清市场机制,将传统调峰市场的市场出清价格向第三方主体市场传导作为价格指导信号,形成虚拟电厂调峰子市场出清结果,因而构建第三方市场主体与传统调峰机组的市场化竞争,实现资源优化配置.此外,基于电动汽车资源设计了虚拟电厂代理参与调峰市场的多种商业运营模型,提高了需求侧资源的积极性.最后,通过算例说明提出的虚拟电厂辅助调峰市场机制降低了系统调峰成本、增加了新能源消纳、实现了绿色价值认证,验证了所设计的商业模型符合激励相容原理,提升了用户参与意愿、有效响应了新能源波动.

能源互联网技术推动了电力产消用户的规模化发展，产消者资源的优化运行与电力市场运营成为改善综合能效和投资收益的重要议题。产消用户的电力负荷精细化预测不但有益于提升分布式资源有限的容量价值，而且还能在售电侧市场全面放开时规避运营风险。首先，综述了产消者电力负荷预测技术及应用场景，提出了事件数据的含义并讨论了事件数据稀疏性带来的行为模式切变对于典型时序预测模型产生的极端预测误差问题。为避免解耦分析多元影响因素下的不确定性，构建了基于预测历史数据置信加权的短时预测修正模型，并分析了修正模型对于极限误差的收敛作用和经济性提升成效。最后，基于真实校园微电网工程运行数据验证了所提方法的有效性。

The growth of variable renewable generations will reduce synchronous inertia, and the demand for managing contingency frequency support is growing. Distributed energy resources (DERs) can provide contingency frequency support via the virtual power plant (VPP). Given that the VPP may cover thousands of DERs with diverse characteristics and response latency, this paper proposes to use equivalent aggregation models for the VPP to participate in contingency reserve services. First, the equivalent aggregation model for the VPP’s aggregated frequency response is developed, accurately and concisely restoring the DER aggregation’s capability to stop the system frequency from falling. On this basis, the VPP’s performance-to-cost map is constructed, which specifies the minimal cost to achieve the same performance as different equivalent aggregation models. In order to encourage DERs to report their response latency accurately, additional latency is penalized based on the resulting cost increase. A sensitivity-based method in the matrix form is developed to estimate the marginal increasing cost due to the increasing latency. Numerical studies are conducted to validate the equivalent aggregation model’s effectiveness and illustrate how latency affects response performance and aggregation cost.

2022年6月15日至24日,澳大利亚国家电力市场(NEM)暂停运行,引起了业界的广泛关注.首先,文中结合NEM暂停前后的运行情况,从市场运行数据分析入手,对NEM暂停运行的紧急状态应对机制进行深入剖析,着重分析澳大利亚电力市场运营机构(AEMO)的应对措施与实施程序.进一步,总结梳理近年来世界各地电力市场紧急状态的案例与应对机制,包括对紧急状态的判定标准和干预手段的相关规定,并对其进行归类与总结,寻找共性的机制要素.最后,基于国际经验对中国当前正在推进的电力市场建设提出紧急状态应对的相关政策建议,包括:认定标准、运行机制、定价和补偿机制、市场恢复机制等方面.

建设新型电力系统需适应大规模、高比例新能源接入，对系统运行灵活性提出更高要求，亟待发掘闲散灵活性资源并进行有效调控。虚拟电厂作为分散资源的高效聚合方式，可为新型电力系统安全运行提供调峰、备用、调频等多种灵活性服务，而如何建设虚拟电厂以充分释放海量分布式资源的可调潜力，是其中关键的技术难点。文中聚焦于虚拟电厂中灵活资源的运行特性、动态构建与可信量化这一关键问题，对虚拟电厂的外特性建模、资源优化组合、服务能力的可信量化等问题进行了详细综述。首先，总结了虚拟电厂灵活资源运行特性对其外特性的差异化支撑能力，并提出了通用的规范化描述方法；然后，立足物理-信息-社会耦合视角，分别分析了虚拟电厂动态构建与可信量化的关键问题及核心技术，并从个体资源和资源集群两个维度分析了不同视角下的耦合关系及关键技术的支撑逻辑；最后，归纳总结了文中主要贡献与虚拟电厂服务新型电力系统的展望与挑战。

为解决现有双馈风电机组频率控制策略不能充分利用转子动能支撑电网频率及风机转速恢复造成的二次频率冲击问题,提出了一种计及转速平滑恢复的双馈风电机组自适应频率控制策略.首先在电网频率支撑阶段,借助指数函数将风电机组频率控制系数和电网频率偏差建立耦合关系,使频率控制系数随频率偏差增加而变大,从而使风电机组在频率支撑阶段释放更多能量,提高频率最低点;其次在风机转速恢复阶段,借助一次递减函数在预设时间内将控制系数平滑减少至零,实现可控的转速恢复,同时消除转速恢复对频率的二次冲击.最后,通过EMTP-RV软件搭建了IEEE 4机2区域的电力系统模型,验证了所提策略的有效性.

"双碳"战略背景下,电力系统面临着清洁化、高可靠、低成本的重大三角矛盾.针对多重不确定性复杂耦合的电力系统,安全可靠的灵活调节能力和绿色电力的弹性消纳裕度成为重要攻克方向.面向新型电力系统的聚合技术架构,使海量、灵活、分散且难观、难测、难控的灵活性"沉睡"资源得以唤醒调动,因其低成本、高弹性成为了新型电力系统的重要可调资源,是关键技术解决方案,但相关工作仍处于起步示范阶段.聚焦国内外灵活资源聚合辅助电力系统运行的典型技术应用场景,梳理了面向新型电力系统聚合技术架构的典型分类,并分析比较了其技术特征,给出了灵活资源参与电力系统运行的架构技术建设启示和发展建议,为我国构建以新能源为主体的新型电力系统提供重要技术支撑.

The development of multi-function complementary technology will further highlight the role of virtual power plants in power grid operation and power market. Therefore, the concept and characteristics of a multi-energy complementary virtual power plant are summarized, and the structure and critical technologies of virtual power plant participating in the power market are discussed from the transaction layer. Under the premise of considering the demand response, a two-layer optimization model of the virtual power plant and user side was established with the net income of the virtual power plant as the upper objective function and the cost of purchasing power on the user side as the lower objective function. Finally, an example is given to verify that the model can improve the operating income of virtual power plant and effectively reduce the power purchase cost of the load.

能源互联网的发展推动了分布式资源配置规模与利用效率的逐步提高，规模化灵活资源虚拟电厂的构建将成为新型电力系统灵活性提升的关键研究领域。文中聚焦于规模化灵活资源可信聚合以及灵活资源辅助电力系统所需的信息-能量-价值耦合互动机理问题，提出了规模化灵活资源虚拟电厂的科学问题、技术路线与理论框架。在此基础上，分别从动态聚合、安全通信、协同调控、可信交易等4个领域，论述了具体的核心关键技术及其关联支撑关系。最后，围绕规模化灵活资源虚拟电厂的技术挑战与创新应用进行了总结和展望。

As the numbers of microgrids (MGs) and prosumers are increasing, many research efforts are proposing various power sharing schemes for multiple MGs (MMGs). Power sharing between MMGs can reduce the investment and operating costs of MGs. However, since MGs exchange power through distribution lines, this may have an adverse effect on the utility, such as an increase in peak demand, and cause local overcurrent issues. Therefore, this paper proposes a power sharing scheme that is beneficial to both MGs and the utility. This research assumes that in an MG, the energy storage system (ESS) is the major controllable resource. In the proposed power sharing scheme, an MG that sends power should discharge at least as much power from the ESS as the power it sends to other MGs, in order to actually decrease the total system demand. With these assumptions, methods for determining the power sharing schedule are proposed. Firstly, a mixed integer linear programming (MILP)-based centralized approach is proposed. Although this can provide the optimal power sharing solution, in practice, this method is very difficult to apply, due to the large calculation burden. To overcome the significant calculation burden of the centralized optimization method, a new method for determining the power sharing schedule is proposed. In this approach, the amount of power sharing is assumed to be a multiple of a unit amount, and the final power sharing schedule is determined by iteratively finding the best MG pair that exchange this unit amount. Simulation with a five MG scenario is used to test the proposed power sharing scheme and the scheduling algorithm in terms of a reduction in the operating cost of MGs, the peak demand of utility, and the calculation burden. In addition, the interrelationship between power sharing and the system loss is analyzed when MGs exchange power through the utility network.

Available rotor kinetic energies from doubly-fed induction generators (DFIGs) in the upstream of a wind farm are different from DFIGs in the downstream due to the non-negligible wake effect. Thus, a variable gain of a droop control method is required for system inertia support because the constant-control gain method weakens the inertia support capability during disturbance. This paper presents an improved droop control method from a wind farm to efficiently utilize the rotating masses of DFIGs for system inertia support considering rotor kinetic energy-based variable-droop characteristics from temporal and spatial viewpoint. DFIGs in the upstream of a wind farm with large rotor speeds release more kinetic energy to the grid for system inertia support; DFIGs in the downstream with low rotor speeds release less kinetic energy to prevent rotor speed from stalling. Simulation results clearly indicate that the proposed method provides better performance in terms of improving the frequency nadir, nadir-based frequency response, and preventing DFIGs from stalling. Therefore, the proposed method improves the capability for wind power integration and facilitates to wind energy accommodation.